Chinese researchers have proposed using an algorithm that simulates the behavior of foraging bees to create a new grid-forming inverter structure. The approach is designed for both grid-connected and isolated inverters.
This algorithm mimics the foraging behavior of bee colonies when looking for nectar. It optimizes numerical problems by transforming them into the problem of finding the best parameter to minimize the objective function. The algorithm moves towards better solutions, but rejects bad solutions.
Grid-forming inverters create adjustable voltage and frequency and can use microgrids without traditional synchronous power generators. They can also act as primary mechanisms to improve network synchronization, especially under low-inertia operating conditions.
The team said the proposed inverter design can perform optimally in both grid-connected and islanded applications. It combines these two different control strategies by using a Fractional Order Proportional-Integral (FOPI) controller – a way to add additional parameters to the algorithm.
“The FOPI controller has a higher degree of freedom compared to traditional PI controllers, and the objective of error minimization can be achieved more efficiently,” it explained, adding that it used Monte Carlo simulations, which are typically used to estimate unpredictable outcomes influenced by random variables by reducing complex processes to a set of basic events and interactions. “A Monte Carlo simulation has been performed to generate the initial optimal search space.”
The controller can also operate according to an adaptive stopping criterion, which, according to the researchers, helps the algorithm to stop early if a solution convergence is found. This makes the algorithm faster and more efficient compared to other different optimization algorithms such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO), which the group also tested with the same inverter design.
PSO is a stochastic optimization method based on swarm movements and intelligence to minimize the CoE of both energy sources in modeling. The GWO algorithm mimics the hierarchy and hunting mechanism of gray wolves in the wild.
The controllers work in quality data (qd) mode and help the inverter to find the optimal solution by reducing the uncertainty from freezing to a suboptimal solution. Their operation was simulated in different scenarios, such as under different island grid reconnection times, and their performance was tested during load changes and line parameter changes.
Through simulation, the scientists ensured that important parameters such as frequency, voltage dq, and real and reactive power reached a steady state or allowable range within the desired time period. Their performance was found to be “satisfactory” at each load level.
The new inverter design is presented in the magazine “An Integrated Lattice Network Following Inverter Fractional Order Controller Based on Monte Carlo Artificial Bee Colony Optimization, published in Energy reports.
